Multipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint identity-by-descent (IBD) probability calculations. We extend the sib-pair multipoint mapping approach of Fulker et al. to general relative pairs. This multipoint IBD method uses the proportion of alleles shared identical by descent at genotyped loci to estimate IBD sharing at arbitrary points along a chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent or by multiple independent lines of descent. Once calculated, the multipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likelihood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accuracy of QTL localization and the increase in power provided by multipoint analysis with 5-, 10-, and 20-cM marker maps. The general pedigree variance component and IBD estimation methods have been implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package.

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies are unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Alcoholism is a complex disease with both genetic and environmental risk factors. To identify genes that affect the risk for alcoholism, we systematically ascertained and carefully assessed individuals in families with multiple alcoholics. Linkage and association analyses suggested that a region of chromosome 4p contained genes affecting a quantitative endophenotype, brain oscillations in the beta frequency range (13-28 Hz), and the risk for alcoholism. To identify the individual genes that affect these phenotypes, we performed linkage disequilibrium analyses of 69 single-nucleotide polymorphism (SNPs) within a cluster of four GABA(A) receptor genes, GABRG1, GABRA2, GABRA4, and GABRB1, at the center of the linked region. GABA(A) receptors mediate important effects of alcohol and also modulate beta frequencies. Thirty-one SNPs in GABRA2, but only 1 of the 20 SNPs in the flanking genes, showed significant association with alcoholism. Twenty-five of the GABRA2 SNPs, but only one of the SNPs in the flanking genes, were associated with the brain oscillations in the beta frequency. The region of strongest association with alcohol dependence extended from intron 3 past the 3' end of GABRA2; all 43 of the consecutive three-SNP haplotypes in this region of GABRA2 were highly significant. A three-SNP haplotype was associated with alcoholism, with P=.000000022. No coding differences were found between the high-risk and low-risk haplotypes, suggesting that the effect is mediated through gene regulation. The very strong association of GABRA2 with both alcohol dependence and the beta frequency of the electroencephalogram, combined with biological evidence for a role of this gene in both phenotypes, suggest that GABRA2 might influence susceptibility to alcohol dependence by modulating the level of neural excitation.